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Qwen/Qwen3-235B-A22B-Instruct-2507

Qwen3-235B-A22B-Instruct-2507 is the updated version of the Qwen3-235B-A22B non-thinking mode, featuring Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.

Tool callingJSON schema
Try Qwen/Qwen3-235B-A22B-Instruct-2507CompareDocs

Specifications

Context window262K tokens
Max outputβ€”
API typechat
AddedMay 27, 2026
Model IDdeepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507
Data retentionNo
Used for trainingNo
Provider locationπŸ‡ΊπŸ‡Έ US
Privacy policyDeepInfra Privacy Policy→

Benchmarks

Released 2025-07-25
Coding Indexcoding
23.2%

Artificial Analysis Coding Index β€” a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
79.0%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
29.5%

Artificial Analysis Intelligence Index β€” a composite of multiple evaluations measuring overall model capability.

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality β€” always test on your own workload.

Pricing

Input / 1M
$0.07
Output / 1M
$0.10
Cache write
β€”
Cache read
β€”
Estimated cost
100K input + 10K output$0.0081
1M input + 100K output$0.0810
10M input + 1M output$0.81

Requesty charges exactly what the upstream provider charges β€” no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

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Frequently asked questions

How much does Qwen/Qwen3-235B-A22B-Instruct-2507 cost?
Qwen/Qwen3-235B-A22B-Instruct-2507 is priced at $0.07 per million input tokens and $0.10 per million output tokens when accessed via Requesty. Requesty charges exactly what the upstream provider charges β€” we don't add markup.
What is the context window of Qwen/Qwen3-235B-A22B-Instruct-2507?
Qwen/Qwen3-235B-A22B-Instruct-2507 has a context window of 262K tokens. That's roughly 350 words of input you can fit in a single prompt.
How does Qwen/Qwen3-235B-A22B-Instruct-2507 perform on benchmarks?
Qwen/Qwen3-235B-A22B-Instruct-2507 scores 91.0% on Math Index, 91.0% on AIME 2025, 84.3% on MMLU Pro. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can Qwen/Qwen3-235B-A22B-Instruct-2507 do?
Qwen/Qwen3-235B-A22B-Instruct-2507 supports tool calling, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use Qwen/Qwen3-235B-A22B-Instruct-2507 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "deepinfra/Qwen/Qwen3-235B-A22B-Instruct-2507". The Quickstart above shows Python, JavaScript and cURL snippets.

Access Qwen/Qwen3-235B-A22B-Instruct-2507 through Requesty

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